Method for validating geological model data over corresponding original seismic data

ABSTRACT

Techniques for generating a geological model from 3D seismic data and rock property data are disclosed. Rock property data and 3D seismic data are received. Based on the rock property data and the 3D seismic data, an adaptive geological model is generated. The adaptive geological model includes a characteristic geological property. Synthetic seismic data is generated from a first region of interest of the adaptive geological model. The synthetic seismic data is adapted to facilitate a comparison between the first region of interest and a corresponding region of interest of the received 3D seismic data. The characteristic geological property is adjusted until the comparison indicates a result that is within a predetermined threshold region of the corresponding value from the rock properties. A validated geologic model is then generated.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a 35 U.S.C. § 371 U.S. National Stage ofInternational Application No. PCT/GB2018/051589, filed on Jun. 11, 2018,which claims priority to Great Britain Patent Application No. 1709332.9,filed Jun. 12, 2017, the entire content of each of which is incorporatedherein by reference.

The present invention relates generally to the field of oil and gasexploration, and in particular to the field of computer aidedexploration for hydrocarbons using geophysical data, such as for exampleseismic data, of the earth. Even more particular, the present inventionrelates to geological modelling from 3D seismic data and optimising theconceptual model to the original corresponding 3D seismic data so as tominimise the uncertainty between the “real” properties and theinterpreted properties derived from the geological model.

INTRODUCTION

In the oil and gas industry, geological data surveys such as, forexample, seismic prospecting and other similar techniques are commonlyused to aid in the search for and evaluation of subterranean hydrocarbondeposits. Furthermore, production geologists are increasingly usingcomputer-based geological models or geomodel (e.g. using 3D geocellularmodelling packages) to represent a reservoir's geology.

One of the challenges of the interpreter(s) is to try and replicateknown or conceptual geological features that are presumed to be presentin the subsurface. The geomodel may then help to more accurately predictthe implications of the geological features in potentially being provenhydrocarbon reserves of commercial value. For example, seismic faciesinterpretation can play a significant role in the initial basinexploration, prospect evaluation, reservoir characterisation, andultimately, field development.

A typical geological model may be used to control the distribution ofrelevant petro-physical well data to provide a basis for accuratevolumetric assessment. However, most of the known conventionalapproaches to geological and property modelling rely on well data todrive the results, as (until very recently) the ability to see andderive information from seismic data was very limited. In particular,high-resolution geological models may be built upon 3D mathematicalmeshes that provide the numerical architecture for building thestructural stratigraphic framework. The model(s) are then generallyconstructed and parameterized through software products that allowprofessional geoscientists to approximate the static state of thereservoir by interpolating or simulating, for example, geologic faciesand their petro-physical properties within a 3D volume. This process is“model driven” from a scenario comprising the conceptual geologicalmodel. The interpolation and simulation algorithms used to fill, forexample, the inter-well space, are performed using workflows based onthese conceptual models and attempt to bind (or link) the results tological rules derived from underlying geologic principles.

While the workflows can vary based on individual interpretation of thedata, the results are generally obligated to respect the observed data.Within a given scenario, it is the interpolation algorithm that isresponsible for providing the best estimate at every grid location andthe simulation algorithm that is responsible for capturing the inherentvariability, providing the basis for any uncertainty analysis.

For example, in the known art, the current geological modelling workflowmay be as follows:

-   -   Build a structural framework (horizons and faults)    -   Use the horizons and faults to build a geo-cellular grid    -   Interpret log data to determine facies types within different        geological layers and zones    -   Perform data analysis to identify trends in data    -   Upscale this information to populate the geo-cellular grid    -   Apply modelling algorithms to extrapolate property values away        from wells—various options    -   Tie to any additional data source such as seismic attributes or        trends

This process can take weeks or months to complete, and usually involvesa simplification of interpreted horizons and/or faults, and may requirea reduction in lateral resolution to create the geocellular grid.

Some of the geological variations that may be seen in well log data aretoo complex to attempt to model. Often, there is additional degradationof the data due to the averaging methods used to assign values to gridcells.

Furthermore, away from the well, petro-physical well data may bedistributed to all cells within the geological model usinggeostatistics. In this process, extrapolation of property values awayfrom a well is often tied to seismic attributes such as, for example,the acoustic impedance.

As can be seen, current methods and modelling techniques includesignificant number of assumptions and approximations that are madeduring the geological modelling workflow and none of these methods ortechniques are capable of providing any kind of quality assurance of themodelled data and interpreted properties. I.e. the interpreter has nowway of knowing how closely the generated geological model matches theoriginal data. Furthermore, none of the available techniques/methods isable to (quickly and easily) assess different assumptions andapproximations, so as to ensure the best fit of the modelled data.

SUMMARY OF THE INVENTION

Preferred embodiment(s) of the invention seek to overcome one or more ofthe above disadvantages of the prior art.

According to a first aspect of the invention there is provided acomputer implemented method for generating a validated geological modelfrom three-dimensional (3D) seismic data and legacy data, the methodcomprising the steps of:

-   (a) receiving said legacy data from a first data source;-   (b) receiving said 3D seismic data from a second data source;-   (c) based on receiving said legacy data and said 3D seismic data,    generating an adaptive geological model from said 3D seismic data,    said adaptive geological model comprising at least one    characteristic geological property;-   (d) generating at least one synthetic seismic data from at least a    first region of interest of said adaptive geological model, the    synthetic seismic data being adapted to determine a qualitative    similarity value between at least said first region of interest of    said adaptive geological model and a corresponding region of    interest of said received 3D seismic data;-   (e) comparing said qualitative similarity value to a corresponding    value within said legacy data;-   (f) adjusting said at least one characteristic geological property    until said qualitative similarity value is within a predetermined    threshold region of said corresponding value from said legacy data,    and-   (g) automatically generating said validated geologic model including    said at least one characteristic geological property that has been    modified to be within said predetermined threshold region of said    corresponding value from said legacy data.

Advantageously, said adaptive geological model may be based on a gridderived from said 3D seismic data. Preferably, said legacy data mayinclude at least one characteristic geological property from any one ofa well-log data and at least one predefined library.

Advantageously, said at least one characteristic geological property maybe assigned from and modified in accordance with at least one probabledata pattern determined utilising machine-learning.

Preferably, said at least one characteristic geological property may beassigned utilising any one of ‘Kriging’, ‘Co-Kriging’, stochasticmodelling, a data from a predefined library, or ‘machine-learning’. Evenmore preferably, said probable date pattern may be determined using atleast one predetermined training data.

Even more preferably, said at least one characteristic geologicalproperty may be at least one rock characteristic.

Advantageously, said adaptive geological model may comprise a pluralityof geological objects.

Advantageously, all of said plurality of geological objects may beadaptively linked to each other.

Advantageously, said plurality of geological objects may include any oneof or any combination of a geobody, a horizon, a fault, and any othersuitable planar geological feature derivable from said 3D seismic data.

Advantageously, in step (e) said at least one synthetic data is comparedto said 3D seismic data utilising any one of visual comparison, atrace-by-trace correlation, a difference between predeterminedcorrelating volumes, spectral comparison.

According to a second aspect of the invention there is provided acomputer system for generating a validated geological model from 3Dseismic data and legacy data by a method according to any one of thepreceding claims.

According to a third aspect of the invention there is provided acomputer readable storage medium having embodied thereon a computerprogram, when executed by a computer that is configured to perform themethod of the first aspect of the invention.

Alternatively, there is provided a method for validating a geologicalmodel to a corresponding 3D seismic data, comprising the steps of:

-   (a) generating an adaptive geological model from said original 3D    seismic data comprising at least one first characteristic geological    property;-   (b) generating at least one synthetic seismic data from at least a    first region of interest of said adaptive geological model that is    adapted to determine a qualitative similarity value to a    corresponding region of interest of said original 3D seismic data;-   (c) comparing said qualitative similarity value to a predetermined    reference value;-   (d) modify said at least one first characteristic geological    property until said qualitative similarity value is within a    predetermined threshold region from said predetermined reference    value.

Advantageously, in the alternative method, in step (d) said at least onefirst characteristic geological property is modified based on at leastone probable data pattern determined using machine learning algorithms.Preferably, said at least one probable data pattern is determinedutilising a predetermined training set of said at least one firstcharacteristic geological property. Even more preferably, said at leastone first characteristic geological property is at least one rockcharacteristic.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the present invention will now be described, byway of example only and not in any limitative sense, with reference tothe accompanying drawings, in which:

FIG. 1 shows an illustration of the workflow of the present invention“closing the loop” between the generated geomodel and the originalseismic data through validation;

FIG. 2 shows (a) an example illustration of adaptive horizons andfaults, and (b) an example illustration of a watertight model createdusing and adaptive framework;

FIG. 3 shows (a) an illustration of additional layers that areautomatically created between horizons, and (b) an illustration of theadditional layering between horizon pairs being displayed as a volume;

FIG. 4 shows an illustration of the distribution of rock propertieswithin each geological layer;

FIG. 5 shows an illustration of sub-seismic sample layers;

FIG. 6 shows an illustration of a geological model at seismic scalerepresenting seismic data;

FIG. 7 shows (a) an illustration of variable density modelling theseismic, and (b) an illustration of an RGB frequency blend proceededdirectly on the variable density;

FIG. 8 shows a simplified illustration of a seismic wave hitting a layerboundary at an angle θ₁, resulting in reflected and transmitted P- andS-waves;

FIG. 9 shows an illustration of rock boundaries, as well as, waveletextraction;

FIG. 10 shows an illustration of an example trace and correspondingextracted wavelet;

FIG. 11 shows an illustration of a geological model that is split intosections/regions;

FIG. 12 shows (a) a close-up of the top-right region of the geologicalmodel in FIG. 11 , and (b) its underlying seismic data;

FIG. 13 shows an illustration of an example learning algorithm utilisedto classify lithology;

FIG. 14 shows an illustration of a sliding window for visual comparisonof the synthetic seismic reflectivity data and corresponding image fromthe real data;

FIG. 15 shows examples (a) and (b) of single layer correlation betweensynthetic and RGB frequency blend, and

FIG. 16 shows an example of a multilayer correlation between syntheticand RGB frequency blend.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT(S)

The exemplary embodiments of this invention will be described inrelation to geological modelling described in any one of EP2867703A,EP2867705A, EP3047311A, EP3001866B and WO2016/124878. However, it shouldbe appreciated that, in general, the method and system of the presentinvention will work equally well with any other geological model createdfrom 2D or 3D seismic data.

For purposes of explanation, it should be appreciated that the terms‘determine’, ‘calculate’ and ‘compute’, and variations thereof, as usedherein are used interchangeably and include any type of methodology,process, mathematical operation or technique. The terms ‘generating’ and‘adapting’ are also used interchangeably describing any type of computermodelling technique for visual representation of a subterraneanenvironment from geological survey data, such as 3D seismic data. Inaddition, the terms ‘vertical’ and ‘horizontal’ refer to the angularorientation with respect to the surface of the earth, i.e. a seismicdata volume is orientated such that ‘vertical’ means substantiallyperpendicular to the general orientation of the ground surface of theearth (assuming the surface is substantially flat), and ‘horizontal’means substantially parallel to the general orientation of the groundsurface of the earth. In other words, a seismic data volume is thereforein alignment with respect to the surface of the earth so that the top ofthe seismic volume is towards the surface of the earth and the bottom ofthe seismic volume is towards the centre of the earth. In addition, theterm ‘time domain’ may also define the vertical direction of the seismictraces with regards to the surface of the earth, whereas the term‘lateral’ may refer to a horizontal displacement with regards to the tosurface of the earth. Furthermore, the term ‘atom’ is generally known bythe person skilled in the art and refers to an adapted wavelet from adictionary of wavelets to generate an analytical model function.

As illustrated in FIG. 1 , the computer system with improvedfunctionality from the present invention “closes the loop” between the3D geological model and the original seismic data. The new conceptenables both the seismic interpretation and the geological concepts tobe validated against the original data and allows different geologicalhypotheses suggested by the data to be examined.

In particular, the purpose of the invention is to make it much easier toQuality Control (QC) and therefore validate any 3D geological model andit's underpinning geological concepts.

A preferred method of the initial geological model building is nowdiscussed.

Model Creation

As shown in FIG. 2 , a “watertight” geological model may be createdusing an adaptive framework as described in any or all of EP2867703A,EP2867705A, EP3047311A, EP3001866B and WO2016/124878. Within theadaptive framework, the interpreted adaptive horizons, faults andgeobodies are linked to one another, and are configured to update “onthe fly” when any one of these adaptive surfaces (i.e. horizons, faultsand geobodies) are modified (e.g. by the interpreter), or theinterpretation is refined.

In many real applications, the geocellular grid (corner point grid),used to model petrophysical and dynamic properties in the reservoir,does not coincide with the seismic grid. In particular, geocellular gridcells are usually larger than the bin size of the seismic survey. In thepresent invention, the automatic refinement/adaption of the surfacesoccurring on the seismic grid, removes the need to generate ageocellular grid. As is known from the currently available prior art,the creation of a geocellular grid can take a significant amount oftime, as the surfaces are adjusted/modified to fit, for example, to acoarser grid. In the present invention, the automatic refinement isbased on the seismic grid making the process of interpretation fasterand more efficient.

As illustrated in FIGS. 3 (a) and (b), within the “watertight” model,additional layers between interpreted horizons and faults can be definedmanually or automatically. I.e., the manually and automatically detectedlayers can be edited so that boundaries can be shifted or layers can becombined or split. This provides a very efficient method to divide thedata into compartments representing different geological units. As isshown in FIG. 4 , the geometries defined by the interpreted andautomatically generated layers may form zones with assigned rockproperties. Layers can be based on user-defined horizons, data-drivensub-horizons or iso-proportional slicing. The advantage of user-definedlayers or horizons is that each layer is manually QC'ed (qualitycontrolled), though at the expense of increased time. Iso-proportionalslicing may be advantageous in really noisy data or complex data (wheresignals are patchy), using other horizons to guide the intermediatelayers through the noisy data.

Also, as the vertical resolution of well log data is finer than seismicdata, within each zone, intermediate subsample layers are defined to bemore closely spaced than the seismic sampling. Rock properties are thenassigned to each of these units or groups of units. FIG. 5 illustrates apreferred method for subsampling the layers using iso-proportionalslicing. The rock properties can then be assigned to each layer directlyfrom any one of a well-data, a pre-defined library of common rockproperties from different regions of the world, or by using machinelearning.

Once a geological model has been created (or a previous geological modelhas been provided), the hypotheses represented by that model arevalidated. During the validation, any one of the whole volume, asub-volume or a slice of the geological model may be examined.

Validation

The validation step provides a fast, practical workflow, that enablescross checking at every stage of the interpretation and 3D modellingworkflow, making it easier to QC and validate both a 3D static model andthe geological concepts that underpin that model.

Here, a synthetic model is created from the geological model usingforward modelling techniques, so as to enable a comparison back to theoriginal data, and also allow different ideas and concepts to be tested.

Various forwarding modelling techniques may be used to generate thesynthetic model(s). A preferred forward modelling method of the presentinvention is as follows:

Forward Modelling

As is known, complex interference patterns occurring in seismic data andRGB frequency colour blends can be related to thickness and impedancevariations. Although variation in bed thickness is a dominant factorcontrolling seismic amplitude and RGB blended frequency decompositioncolour responses, subtle lithological changes (presented as differencesin acoustic impedance contrast) can be differentiated as a second ordereffect.

Referring now to FIGS. 6 and 7 , forward modelling is used to model thecomplex interference patterns occurring in seismic. Forward modellinggenerates synthetic variable density volumes that are derived from earthmodels or other geological models. Although, a variety of forwardmodelling algorithms are suitable for generating synthetic variabledensity volumes, the preferred algorithms used with the presentinvention are the Aki-Richards approximation to the Zoeppritz equations.

Aki-Richards Approximation for Forward Modelling

Modelling seismic wave energy as it partitions at the interface of twodifferent rock layers is a fundamental part of forward modelling. Theuse of the Zoeppritz equations to model rock boundaries, as well as, anumber of linearized approximations to the Zoeppritz equations, havebeen accepted for a long time.

An overview of the Zoeppritz equations is now given, together with adescription of the Aki-Richards approximation.

The Zoeppritz equations calculate the amplitudes of transmitted andreflected components of the P- (longitudinal) and S- (transverse) wavesfor a seismic wave incident on a boundary between two different rocklayers.

The full Zoeppritz system comprises four equations in four unknowns. Theequations can be solved, but they do not provide an intuitiveunderstanding of the relationship between the coefficients and the rockproperties (e.g. density (p), P-wave velocity (Vp), S-wave velocity(Vs)) either side of the boundary. Of the four results calculated bythese equations, the amplitude of the reflected P-wave is the mostimportant characteristic for the forward modelling implementation withinthe method of the present invention.

In practice, there are numerous approximations for the Zoeppritzequations that are simpler and more intuitive to understand in terms ofthe rock properties. “‘Elastic-wave AVO methods’, by BillGoodway—PanCanadian Energy Corporation, [2002]” provides theAki-Richards (1980) approximation. Again, this is the preferred approachin the method of the present invention. It is a three-term equation interms of the ratios of density, P-wave velocity and s-wave velocitybetween the two surfaces, whose boundary the equation is applied to.

FIG. 8 shows a simplified illustration of a wave hitting a boundary atan angle θ₁, resulting in reflected and transmitted P- and S-waves. Thereflection coefficient calculated using the Aki-Richards approximationis that of the reflected P-wave (Rpp(θ₁)), given as:

$\begin{matrix}{{{{Rpp}\left( \theta_{1} \right)} = {{\frac{1}{2}\left( {\frac{\Delta\; V_{p}}{V_{p}} + \frac{\Delta\rho}{\rho}} \right)} - {2\frac{V_{s}^{2}}{V_{p}^{2}}\sin^{2}{\theta_{1}\left( {{2\frac{\Delta\; V_{s}}{V_{s}}} + \frac{\Delta\rho}{\rho}} \right)}} + {\frac{1}{2}\tan^{2}\theta_{1}\frac{\Delta\; V_{p}}{V_{p}}}}};} & \left\lbrack {{Eq}.\mspace{14mu} 1} \right\rbrack\end{matrix}$

where:Δx=x ₂ −x ₁x=½(x ₁ +x ₂)

for x representing each of ρ, Vp, Vs.

The other three results that can be obtained from both, the Zoeppritzand Aki-Richards equations, are the amplitudes of the transmitted P-waveand of the reflected and transmitted S-waves.

The angle of the transmitted P-wave is determined by Snell's law as:

$\begin{matrix}{{\frac{\sin\mspace{11mu}\theta_{1}}{{Vp}_{1}} = \frac{\sin\mspace{11mu}\theta_{2}}{{Vp}_{2}}};} & \left\lbrack {{Eq}.\mspace{14mu} 2} \right\rbrack\end{matrix}$

Snell's law is used in ray tracing algorithms, which take into accountthe impact of different layers in a multi-layered model. In suchalgorithms, the transmitted P-wave from the first boundary encounteredwould determine the angle at which the wave with an initial angle of θ₁hits the second boundary.

However, the method of the present invention does not account for this,but instead considers each boundary independently of the rock layersthat lie above it. Whilst this provides a less accurate model, it doeshave the property that a boundary between the same two rock types willalways yield the same reflection coefficient, irrespective of where itsits in the model, so that changing the upper layers of the model willnot affect the synthetic seismic result in the lower part of the model.

Where multiple angles of incidence are used, Rpp(θ) is calculated for θat 1° [deg] intervals from the minimum to the maximum angle in therange. The resulting values are averaged with a mean function to yield asingle reflection coefficient, Rpp(θ_((min,max))), for an angle rangefrom θ_(min) to θ_(max).

A seismic trace s(θ) for an incidence angle θ, is defined from thereflectivity Rpp(θ) and the wavelet ω by:s(θ)=Rpp(θ)*w;  [Eq. 3]

The earlier Aki-Richards approximation of Zoeppritz equations is used tocompute the reflectivity (Rpp) from the rock properties.

Generic wavelets such as a Ricker or Gabor can be used to then generatea synthetic trace, however, to more accurately model the seismic,extracted wavelets may be used. FIG. 9 shows an illustration of threerock layers and its boundaries, as well as, wavelet extraction.

Wavelet Extraction

Wavelets are required to model the seismic wave energy as it partitionsat the interface of two different rocks. The type of wavelet (such asRicker, Gabor or any other suitable one) and its dominant frequencygreatly influence the seismic reflectivity data produced from forwardmodelling.

To obtain more accurate models, wavelets extracted directly from theseismic is the preferred method. For example, wavelet extraction usesthe data from a predetermined number of in-lines and cross-lines withina region of interest (ROI). A variety of techniques are available forwavelet extraction, including, for example, autocorrelation. Theautocorrelation of the signal may use a rescaled version of theautocorrelation of the wavelet. This technique uses the Wiener-Khinchintheorem and extracts a zero phase wavelet. The spectrum of the waveletcan be computed from the spectrum of the autocorrelation of the signal:F(w)=√{square root over (|F(C _(ww))|)}=B√{square root over (|F(C_(ss))|)};  [Eq. 4]

for some constant, B.

And the wavelet is computed by inverse Fourier transform:w=B·F ⁻¹(√{square root over (|F(C _(ss))|)});  [Eq. 5]

The final wavelet can be normalised by dividing it by its maximum value.FIG. 10 shows an illustration of an example trace and its extractedwavelet.

Parameters

Control over the synthetic generation of properties, such as frequencyand phase of the wavelet, together with the range of angles,contributing to model and layer thicknesses and allow for the validityof the model to be examined.

Multiple realisations of the synthetic data can be produced to examinethe effect of both changing the imaging parameters, but moreimportantly, to compare the effect of changing the layer properties.

In the present invention, an example driven approach is applied toeasily compare the effect of changing parameters.

Similarly, frequency decomposition based RGB blends may be generatedfrom the synthetic data, therefore, allowing the impact of changing thefrequency decomposition parameters on the result to be examined. I.e.how the frequency response differs, if the rock layer properties arechanged, or how the frequency response may change, if the parametersused to generate the synthetic data are changed.

Comparisons can then be made between (i) what is observed in thesynthetic model and (ii) the results generated using the same frequencydecomposition parameters on the original seismic data.

This level of interaction provides a unique and very easy to applymethodology for determining, whether or not, the geological model andthe hypotheses used in defining it really are supported by the originalseismic data.

Rock Property Propagation

Assigning rock properties within the model layers can be achieved usingany one of the following techniques:

a) Kriging:

Kriging is an interpolation method that allows for creation of a modelbased on sparse data from, for example, multiple wells within a region.The aim is to “fill out” (i.e. populate) a 3D model, using the knownwell data, each weighted using spatial covariance to account for thedistance of the target from each known data point. Kriging provides thebest interpolation of the available points, as measured by minimisingits associated error variance.

The general formula for Kriging, so as to estimate a value at an unknownpoint {circumflex over (Z)}(s₀), can be given as:{circumflex over (Z)}(s ₀)=Σ_(i=1) ^(N)λ_(i) Z(s _(i));  [Eq. 6]

where Z(S_(i)) are the measured values at N known locations and λ_(i),are unknown weights, to be determined, summing to ‘1’. Kriging variesfrom other simpler methods in that these weights are based on thespatial arrangement of the known data points, rather than just theirdistance from the target. This allows for isolated known data points tobe weighted more than clusters of points, which may contribute moresimilar and sometimes redundant information.

Variations of the Kriging technique allow for additional information tobe used to determine the weights λ_(i).

b) Co-Kriging:

Co-Kriging is an extension of Kriging, which uses multiple attributes ina multi-variate system to produce an estimate of unknown points.Secondary abundant data sources (such as seismic data) that are highlycorrelated can be incorporated into the measure. The secondary data isthen utilised to guide the interpolation of the primary sparse welldata.

c) Stochastic Modelling:

The mathematically ‘best’ interpolation path is not always the mostsuitable as a realistic solution. Stochastic modelling creates multipleequally probably realisations of a model, each of which fits the sparsewell data, but interpolates between them in different ways. Creatingmany such realisations allows for a greater choice and investigation ofsolutions, which would never be reached by so called ‘safe’ methods,such as Kriging, which always seek to minimise the error.

The average of many realisations should produce a result very similar tothat of Kriging, smoothing out all the differences from each model.Error estimates and uncertainty analysis may also be derived from thesemultiple realisations. Each individual model, however, will be noisierthan the “Kriged” result, but these can be smoothed after extracting therequired statistics.

d) Base Library:

In exploration regions where no well data is available, assumptions ofthe rock properties may be made. A base library may be provided withcommon rock properties retrieved from different regions of the world.The common rock properties may be assigned to each model layer (e.g.facies) using multi attribute (including frequency magnitude volumes)volumes and facies classification. The validity of the rock propertyassignment may then be assessed by QC'ing (Quality Controlling) thegeomodel back against the original seismic data.

e) Machine Learning:

Machine Learning is a process where computer algorithms “find” patternsin the data, enabling the computer to predict probable outcomes. In anaspect of the present invention Machine Learning is utilised toinvestigate complex/high dimensional parameter spaces and determine themost probable rock properties observed in the seismic data, and then toassign the predicted rock properties to each geological layer.

Machine Learning is therefore used to predict the rock propertiesobserved in the seismic data by forward modelling each geological layermodel with a range of common rock properties (within each layer),forming a training set of known labelled results.

Effectively, each variable density generated within the training setforms a “ground truth” in which information such as frequencydecomposition magnitude volumes, facies classification volumes andattribute volumes (such as Quadrature, Envelope, Instantaneous Phase)are used as input features to train learning algorithms, such as, forexample, a deep learning neural network.

To reduce the complexity and variability of the classification problem,the geological model that is compared back to the seismic can be splitinto smaller regions. These regions could be divided into blocks(sub-volumes), and or slices as shown in FIGS. 11 and 12 .

Additionally, the classification problem can be simplified even furtherby isolating the variability of the rock properties (Density, Vp, Vs)from other elements of the synthetic seismic data generation, such as,layer boundary and wavelet characteristics.

To this end, it is assumed that the model layers in the geological modeldefine the rock lithology boundaries and not the seismic reflectivity,and that an appropriate wavelet is used, preferably an extracted waveletfrom the seismic region of interest.

As the selected wavelet and thickness between layer boundariessignificantly influences the generation of the seismic data, the degreesof freedom (DOF) of a synthetic model are reduced by fixing two out ofthe three forward modelling components, i.e. wavelet and thickness ormodel layer (for each region of interest).

Furthermore, for each layer within the geological model, a training setis built “on the fly”, comprising, inter alia, an extracted seismicwavelet, the layer boundaries from the geological model, and varyingrock properties (Density, Vp, Vs).

Different rock properties sequences are then used to create the trainingset for each zone. These sequences are defined using empirical knowledgeof common rock sequencing (such as shale/sand/shale) from differentparts of the world. Within each sequence, the full range of expectedDensity, Vp, Vs values for each rock within the sequence is used togenerate a variable density for each combination. This process may berepeated to include fluid substitution.

As illustrated in FIG. 13 , using then the labelled training set ofcommon lithologies within a geological model, mimicking the lithologyboundaries observed within the seismic, a learning algorithm, such as adeep learning neural network, is used to extrapolate new features fromthe limited set of features contained in the training set (such asfrequency decomposition magnitude volumes, facies classification volumesand attribute volumes) to classify lithology types (sand, shale etc.).

Having now trained a learning algorithm to classify rock lithologywithin a particular zone, features extracted from the corresponding realdata are fed into the learning algorithm to obtain a lithologyprediction. The lithology prediction is then used to populate theappropriate layer within the geological model.

QC Step:

To now validate a rock property assigned geological model to theoriginal seismic data, an assessment method is required. The assessmentmethod compares the geological model, to which the lithologies have beenassigned, by either Geostatistical, Base Library or Machine Learningpropagation or any other method of assignment.

The synthetic model generated from the geological model can now becompared back to the original seismic data, using any one of a number ofmethods, such as, for example:

a) Visual Comparison

Referring now to FIG. 14 , using an ineractive display such as a slidingwindow, a visual comparison of the synthetic seismic reflectivity data(or derived volumes such as frequency decomposition blends or otherseismic attributes) and the corresponding imagary from the real data canbe reviewed and compared.

b) Independent Trace-by-Trace Correlation

A full trace-length measure of similarity is calculated between theoriginal seismic data and the synthetic model, with per-tracecorrelation, lateral granularity/localisation, but no verticallocalisation.

c) Volume Difference

The original seismic data and synthetic data are compared as a whole, orwithin a series of sub-volumes. The comparison could be based directlyon the seismic reflectivity data (i.e. synthetic model), or on any otherseismic attribute, such as, envelope. This is a simple but noisyindicator, as the differences are per-voxel and very localised.

d) Spectral Comparison

A Fast Fourier transform is applied over both, the seismic and syntheticmodel, from full volumes/sub volumes/slices, and use different levels ofgranularity and averaging to compare the spectral content of the modeland the seismic data. This would produce a global measure of spectralcontent differences, but without localisation within the model.

e) Layer Based

Referring now to FIGS. 15 and 16 , an event or layer based measure ofsimilarity could be formed by breaking down the full volume, sub volumeor slice into its layers, and an overall correlation factor is computedfrom the correlation between the original seismic and synthetic seismicdata for each point within each layer. The correlation may occur for asingle layer as shown in FIG. 15 or multiple layers as shown in FIG. 16.

f) Wavelet Based

Matching pursuit decomposition techniques (e.g. GeoTeric™'s HighDefinition Frequency Decomposition), which characterise a signal bymatching it to a set of wavelets can also be used to provide estimatesof the correlation between the original seismic data and the syntheticdata created from the model.

The wavelet matching uses, for example, a dictionary of wavelets thatare matched to an input signal, by varying the wavelet scale,modulation, amplitude and phase. Correlations can then be made betweenthe original seismic data and the synthetic seismic data by comparingthese parameters. The correlation measurements may be done on a point bypoint basis, by comparing sections of a trace or by comparing sectionsof multiple traces. Most importantly, good correlation between some ofthe parameters but not others can be used as a diagnostic to determinewhich parameters of the model might be incorrect, e.g. if scale,modulation and amplitude of the matched wavelets show a goodcorrelation, but the phase of the matched wavelets does not correlatewell, then the problem is likely to be an error in the thickness valuethat has been assigned to the layer in the model.

Machine Learning:

Taking metrics from one or more of the aforementioned QC (QualityControl) approaches, Machine Learning techniques may be used to classifygood matches between the original seismic data and the synthetic datacreated from the geological model. Classifying the fit, by comparingsections of a trace, or by comparing sections of multiple traces.

It will be appreciated by persons skilled in the art that the aboveembodiment has been described by way of example only and not in anylimitative sense, and that various alterations and modifications arepossible without departing from the scope of the invention as defined bythe appended claims.

The invention claimed is:
 1. A computer implemented method for generating a validated geological model from three-dimensional (3D) seismic data and rock property data that is based on one or more of well-logging data, a predefined library, or machine learning data, the method comprising the steps of: (a) receiving rock property data from a first data source; (b) receiving 3D seismic data from a second data source; (c) based on the rock property data and the 3D seismic data, generating a geological model, wherein the geological model comprises a characteristic geological property; (d) generating a synthetic model from the geological model, wherein the synthetic model is adapted to facilitate a comparison between selected data from the synthetic model and corresponding data from the 3D seismic data, and wherein the comparison further provides an indication detailing how the synthetic model changes relative to the geologic model when the characteristic geological property changes; (e) comparing the selected data from the synthetic model against the corresponding data from the 3D seismic data, wherein, as a result of comparing the selected data from the synthetic model against the corresponding data from the 3D seismic data, the indication is provided, where the indication details how the synthetic model changes relative to the geologic model when the characteristic geological property changes; (f) adjusting the characteristic geological property until the indication reflects a value that is within a predetermined threshold; and (g) generating a validated geological model that includes the characteristic geological property, which has been adjusted until the indication reflects the value that is within the predetermined threshold, wherein the synthetic model is a multi-layered model having the following property: a first property in which a boundary that exists in the synthetic model and that is between two rock layers of a same type always yields a same reflection coefficient irrespective of where the boundary sits in the synthetic model.
 2. The method of claim 1, wherein the geological model is based on a grid derived from the 3D seismic data.
 3. The method of claim 1, wherein the rock property data includes the predefined library.
 4. The method of claim 1, wherein the characteristic geological property is assigned from and modified in accordance with a probable data pattern determined utilizing machine-learning.
 5. The method of claim 4, wherein the characteristic geological property is assigned utilizing any one of Kriging, Co-Kriging, stochastic modelling, a data from a predefined library, or machine-learning.
 6. The method of claim 4, wherein the probable data pattern is determined using predetermined training data associated with the machine-learning.
 7. The method of claim 1, wherein the characteristic geological property is a rock characteristic.
 8. The method of claim 1, wherein the geological model comprises a plurality of geological objects.
 9. The method of claim 8, wherein all of the plurality of geological objects are linked to each other.
 10. The method of claim 8, wherein the plurality of geological objects includes one or more of a geobody, a horizon, or a fault.
 11. The method of claim 1, wherein comparing the selected data from the synthetic model against the corresponding data from the 3D seismic data is performed via one or more of visual comparison, a trace-by-trace correlation, a difference between predetermined correlating volumes, or a spectral comparison.
 12. The method of claim 5, wherein the probable data pattern is determined using predetermined training data associated with the machine-learning.
 13. The method of claim 9, wherein the plurality of geological objects includes one or more of a geobody, a horizon, or a fault.
 14. The method of claim 1, wherein the geological model is generated using a forward modeling technique that generates synthetic variable density volumes.
 15. The method of claim 14, wherein implementation of the forward modeling technique results in a generation of an amplitude of a reflected P-wave.
 16. The method of claim 14, wherein the synthetic model further has the following property: as a result of the first property, a second property in which changes to an upper part in the synthetic model relative to the boundary has no impact on a synthetic seismic result to a lower part in the synthetic model relative to the boundary. 